On the expressivity of embedding quantum kernels
Elies Gil-Fuster, Jens Eisert, Vedran Dunjko

TL;DR
This paper investigates the expressivity of embedding quantum kernels, demonstrating their universality for broad classes of kernels and exploring the potential for more complex quantum kernel families.
Contribution
It proves the universality of embedding quantum kernels for shift-invariant and composition kernels, and discusses the potential for new quantum kernel families.
Findings
Embedding quantum kernels are universal for shift-invariant kernels.
Embedding quantum kernels are also universal for composition kernels.
Open questions remain for more exotic quantum kernel families.
Abstract
One of the most natural connections between quantum and classical machine learning has been established in the context of kernel methods. Kernel methods rely on kernels, which are inner products of feature vectors living in large feature spaces. Quantum kernels are typically evaluated by explicitly constructing quantum feature states and then taking their inner product, here called embedding quantum kernels. Since classical kernels are usually evaluated without using the feature vectors explicitly, we wonder how expressive embedding quantum kernels are. In this work, we raise the fundamental question: can all quantum kernels be expressed as the inner product of quantum feature states? Our first result is positive: Invoking computational universality, we find that for any kernel function there always exists a corresponding quantum feature map and an embedding quantum kernel. The more…
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Taxonomy
TopicsMachine Learning and ELM · Neural Networks and Reservoir Computing · Quantum Computing Algorithms and Architecture
